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An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG
Name
syllable_classification_from_EEG.pdf
Description
Accepted version
Size
1.03 MB
Format
Adobe PDF
Checksum (MD5)
5b6491719ecbdc6c84197e94a03e60fe
Author(s)
Sharon, Rini A.
Narayanan, Shrikanth
Sur, Mriganka
Murthy, Hema A.
Date Issued
May 2019
Publisher
IEEE
Citation
Sharon, Rini A., Narayanan, Shrikanth, Sur, Mriganka and Murthy, Hema A. 2019. "An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG."
Version
Author's final manuscript
Abstract
© 2019 IEEE. Clinical applicability of electroencephalography (EEG) is well established, however the use of EEG as a choice for constructing brain computer interfaces to develop communication platforms is relatively recent. To provide more natural means of communication, there is an increasing focus on bringing together speech and EEG signal processing. Quantifying the way our brain processes speech is one way of approaching the problem of speech recognition using brain waves. This paper analyses the feasibility of recognizing syllable level units by studying the temporal structure of speech reflected in the EEG signals. The slowly varying component of the delta band EEG(0.3-3Hz) is present in all other EEG frequency bands. Analysis shows that removing the delta trend in EEG signals results in signals that reveals syllable like structure. Using a 25 syllable framework, classification of EEG data obtained from 13 subjects yields promising results, underscoring the potential of revealing speech related temporal structure in EEG.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/ICASSP.2019.8683572
